Throughout the weather forecasting enterprise, increased computing power and automated numerical weather-prediction guidance is challenging the traditional role of the human forecaster. In many cases, automated forecast methods handle day-to-day weather forecasting scenarios quite well, with little or no human intervention necessary.
In forecasting situations where weather conditions vary greatly from climatological normals, however, automated methods often fall short. In these situations, human intervention may play a major role in the forecast process and the accuracy of forecasts, such as identifying anomalous events that may have a high impact on the consumer public.
Therefore, a need exists to identify and train forecasters to recognize those situations where human forecasters should focus their attention and energies to improve upon the automated guidance. There is also a need for a real time monitoring and verification system that provides verification information to forecasters so that forecasters can focus their efforts to those weather situations where human intervention can add value to the computer model guidance.